Prediction of protein structural classes using hybrid properties.
In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp.
Li
Wenjin
W
CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Lin
Kao
K
Feng
Kaiyan
K
Cai
Yudong
Y
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis; D002627:Chemistry, Physical; D030562:Databases, Protein; D057927:Hydrophobic and Hydrophilic Interactions; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
D000596:Amino Acids; D011506:Proteins